Abstract: Embodiments of the invention may provide a personalized fit prediction to a particular consumer about a particular product. Personalization of the fit prediction may be accomplished by processing various types of data, including data about consumers, products, and previous purchases by consumers. For example, this data may be used to generate a model (e.g., set of rules), which can then be applied to predict whether and/or how well a particular product will suit a particular consumer.

Abstract: Some embodiments of the invention provide techniques for recommending a size of a subject item to fit a subject consumer. In some embodiments, clusters of consumers with fit characteristics similar to the subject consumer are identified, using one or more data clustering algorithms, based on any of numerous consumer attributes (e.g., self-reported and/or inferred height, weight, body shape, body characteristics, and/or purchase histories (e.g., consumers with high overlap in terms of sets of products purchased)). Information on other consumers in the cluster may be analyzed to draw conclusions on how different sizes of the subject item may fit the subject consumer. For example, the purchase history of other members of the cluster may be analyzed to determine whether other members purchased a particular size of the item, and if so, the size purchased by the other members may serve as a basis to recommend a size that may best fit the consumer.

Abstract: Embodiments of the invention may provide a personalized fit prediction to a particular consumer about a particular product. Personalization of the fit prediction may be accomplished by processing various types of data, including data about consumers, products, and previous purchases by consumers. For example, this data may be used to generate a model (e.g., set of rules), which can then be applied to predict whether and/or how well a particular product will suit a particular consumer.

Abstract: Some embodiments of the invention provide techniques for recommending a size of a subject item to fit a subject consumer. In some embodiments, clusters of consumers with fit characteristics similar to the subject consumer are identified, using one or more data clustering algorithms, based on any of numerous consumer attributes (e.g., self-reported and/or inferred height, weight, body shape, body characteristics, and/or purchase histories (e.g., consumers with high overlap in terms of sets of products purchased)). Information on other consumers in the cluster may be analyzed to draw conclusions on how different sizes of the subject item may fit the subject consumer. For example, the purchase history of other members of the cluster may be analyzed to determine whether other members purchased a particular size of the item, and if so, the size purchased by the other members may serve as a basis to recommend a size that may best fit the consumer.

Abstract: Some embodiments of the invention enable a buyer to shop for and purchase items for a recipient with confidence. For example, some embodiments of the invention enable a recipient to create a profile that may be accessed to make recommendations to a buyer on items deemed likely to suit the recipient in any of numerous ways (e.g., those deemed likely to fit the recipient, be age-appropriate, reflect the recipient's style, suit the recipient's fit preferences, conform to the recipient's measurements, etc.). In some embodiments, a recipient may indicate that certain, or all, aspects of the profile be kept private, so that the buyer need not know information about the recipient such as his/her body measurements, fit preferences, style preferences, and/or other information to be able to shop confidently for the recipient.

Abstract: Some embodiments of the invention provide techniques for recommending a size of a subject item to fit a subject consumer. In some embodiments, clusters of consumers with fit characteristics similar to the subject consumer are identified, using one or more data clustering algorithms, based on any of numerous consumer attributes (e.g., self-reported and/or inferred height, weight, body shape, body characteristics, and/or purchase histories (e.g., consumers with high overlap in terms of sets of products purchased)). Information on other consumers in the cluster may be analyzed to draw conclusions on how different sizes of the subject item may fit the subject consumer. For example, the purchase history of other members of the cluster may be analyzed to determine whether other members purchased a particular size of the item, and if so, the size purchased by the other members may serve as a basis to recommend a size that may best fit the consumer.

Abstract: Some embodiments of the invention determine whether a particular item is likely to suit a consumer from a fit and/or style standpoint, using objective data produced as a result of the consumer's experiences. For example, some embodiments of the invention analyze information regarding a consumer's experiences with certain products (e.g., purchase and return history, identification of “favorite” items, etc.) and data regarding attributes of those items (e.g., technical dimension data, stylistic and fit attributes, etc.) to determine the consumer's measurements and fit and/or style preferences, so that a prediction may be made regarding how a particular size of an item may suit the consumer.